{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMAGES_PATH=os.path.join(os.getcwd(),\"static/\")\n",
    "if not os.path.exists(IMAGES_PATH):\n",
    "        os.makedirs(IMAGES_PATH)\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
    "    path = os.path.join(IMAGES_PATH, fig_id + \".\" + fig_extension)\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format=fig_extension, dpi=resolution)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'COL_NAMES': ['label', 'data'],\n",
       " 'DESCR': u'mldata.org dataset: mnist-original',\n",
       " 'data': array([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ..., \n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\n",
       " 'target': array([ 0.,  0.,  0., ...,  9.,  9.,  9.])}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_mldata\n",
    "mnist = fetch_mldata('MNIST original')\n",
    "mnist\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(70000, 784)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X, y = mnist[\"data\"], mnist[\"target\"]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_digits(instances, images_per_row=10, **options):\n",
    "    size = 28\n",
    "    images_per_row = min(len(instances), images_per_row)\n",
    "    images = [instance.reshape(size,size) for instance in instances]\n",
    "    n_rows = (len(instances) - 1) // images_per_row + 1\n",
    "    row_images = []\n",
    "    n_empty = n_rows * images_per_row - len(instances)\n",
    "    images.append(np.zeros((size, size * n_empty)))\n",
    "    for row in range(n_rows):\n",
    "        rimages = images[row * images_per_row : (row + 1) * images_per_row]\n",
    "        row_images.append(np.concatenate(rimages, axis=1))\n",
    "    image = np.concatenate(row_images, axis=0)\n",
    "    plt.imshow(image, cmap = matplotlib.cm.binary, **options)\n",
    "    plt.axis(\"off\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving figure mnist_digits_plot\n"
     ]
    },
    {
     "data": {
      "image/png": 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oz16TlNQyKWqBBRYAQmLTqVOnTMam9EzvObX80GdWjaMhJEFx2vNW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0w95rrrrgPg0EMPLcSwC4K+619++QWAq666CoDrr78egCOPPDK1rq6OF1544SSHWCPvv/8+kL4d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\nLLOXs2b9S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iluycrJ1iiK2j/23ntvAPbff38g+ATzRN2HlBGqTM0k8Xktuc46huWX22ij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\nMBkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdb92367150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]\n",
    "plot_digits(example_images, images_per_row=10)\n",
    "save_fig(\"mnist_digits_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
